Welcome to ICDE'07 Istanbul, Turkey 2007 IEEE 23rd International Conference on Data Engineering
(ICDE 2007)
April 15-20, 2007
The Marmara Hotel, Istanbul, Turkey
Sponsored by the IEEE Computer Society
        General Info
Important Dates
Call For Papers
Call For Participation

Organization Committee
Program Committee

        Other Calls
Call for Industrial Papers
Call for Panels
Call for Seminars
Call for Workshops
Call for Demos

· Overview
· Keynote Speeches
· Research Sessions
· Industrial Sessions
· Poster Sessions
· Adv. Tech. Seminars
· Demo Sessions
· Full Papers
· Poster Papers

List of Workshops

Advanced Technology Seminars

Graph Mining - Motivation, Applications and Algorithms (3 hrs)
Presenter: Ehud Gudes, Ben-Gurion University

Techniques for Similarity Searching in Multimedia Databases (1.5 hrs)
Presenter: Hanan Samet, University of Maryland - College Park

Virtualization and Databases: State of the Art and Research Challenges (3 hrs)
Presenters: Ashraf Aboulnaga, University of Waterloo - Christiana Amza, University of Toronto - Ken Salem, University of Waterloo

Personalized Systems: Models and Methods from an IR and DB perspective (3 hrs)
Presenters: Yannis Ioannidis and Georgia Koutrika, University of Athens

An Introduction to Knowledge Discovery Applications and Challenges in Life Sciences (3 hrs)
Presenter: Limsoon Wong, National University of Singapore

Graph Mining - Motivation, Applications and Algorithms

Ehud Gudes
Ben-Gurion University, Israel

Whereas data-mining in structured data focuses on frequent data values, in semi-structured and graph data mining, the issue is frequent labels and common specific topologies. Here, the structure of the data is just as important as its content. We study the problem of discovering typical patterns of graph data. The discovered patterns can be useful for many applications, including: compact representation of the information contained in a source, a road map for browsing and querying information sources, finding common structures of strongly connected groups in social networks and in several scientific domains like finding frequent molecular structures.
The discovery task is impacted by structural features of semistructured data in a non-trivial way, making traditional data mining approaches inapplicable. Difficulties result from the complexity of some of the required sub-tasks, such as graph and sub-graph isomorphism.In recent years the topic arises much interest, and there is an annual workshop: International Workshop on Mining Graphs, Trees and Sequences dedicated to it.
This seminar will discuss first the motivation and possible applications of Graph mining, and then will survey in detail the recent algorithms for this task, including: FSG, GSPAN and other recent algorithms by the Presentor. The differences between graph mining in the single graph setting and in the transaction setting will be described, and the problematic issue of Support in the single graph setting will also be discussed in detail. Finally, future work and directions for further research on this subject will be outlined.

Short Bio
Ehud Gudes, a native of Israel received his BSc and MSc from the Technion - Israel Institute of Technology, and his PhD in Computer and Information Science from the Ohio State University in 1976. Following his PhD, he worked both in academia (Penn State University, Ben-Gurion University,), where he did research in the areas of Database systems and Data security, and in Industry (Wang Laboratories, National Semiconductors, Elron, IBM Research, Bell Laboratories), where he researched various issues in databases and architecture, developed Query languages, CAD Software, and Expert systems for Planning and Scheduling. He has published over 80 papers in the above general areas, and was the program chair of three international conferences, including IFIP WG11.3 2002 international conference on database security in Cambridge, UK. Prof. Gudes is currently a member professor in computer science at Ben-Gurion University in Israel, and is the head of the Software engineering program at Ben-Gurion University. His research interests are: Knowledge and databases, Data Security and Privacy and Data mining, especially Graph mining. In the graph mining area, he has published several conference papers two recent journal papers and he and his student N. Vanetick were the first to define an admissible support measure for single graph databases.

Techniques for Similarity Searching in Multimedia Databases

Hanan Samet
University of Maryland

Similarity searching is a crucial part of retrieval in multimedia databases used for applications such as pattern recognition, image databases, and content-based retrieval. It involves finding objects in a data set $S$ that are similar to a query object $q$ based on some distance measure $d$ which is usually a distance metric. The search process is usually achieved by means of nearest neighbor finding.
Existing methods for handling similarity search in this setting fall into one of two classes. The first is based on mapping to a vector space. The vector space is usually of high dimension which requires special handling due to the fact indexing methods do not discriminate well in such spaces. In particular, the query regions often overlap all of the blocks that result from the decomposition of the underlying space. This has led to some special solutions that make use of a sequential scan. An alternative is to use dimension reduction to find a mapping from a high-dimensional space into a low-dimensional space by finding the most discriminating dimensions and then index the data using one of a number of different data structures such as k-d trees, R-trees, quadtrees, etc. The second directly indexes the objects based on distances making use of data structures such as the vp-tree, M-tree, etc.
This seminar is organized into four parts that include an overview as well as cover the basic concepts outlined above: indexing low and high dimensional spaces, distance-based indexing, and nearest neighbor searching.

Short Bio

Hanan Samet is a Professor of Computer Science at the University of Maryland where he leads a number of research projects on the use of hierarchical data structures for database applications involving multimedia data such as spatial and image databases. His research group has developed the SAND spatial browser, the VASCO system of JAVA applets for visualizing and animating spatial indexes (http://www.cs.umd.edu/~hjs/quadtree/index.html), and the MARCO system for map retrieval by content which enables pictorial queries on a symbolic image database system. He is the author of the recent book "Foundations of Multidimensional and Metric Data Structures" published by Morgan-Kaufmann, San Francisco, CA, 2006 (http://www.mkp.com/multidimensional), as well as the texts "Design and Analysis of Spatial Data Structures" and "Applications of Spatial Data Structures: Computer Graphics, Image Processing and GIS" published by Addison-Wesley, Reading, MA, 1990. He has a Ph.D from Stanford University. He is a Fellow of the ACM, IEEE, and the International Association of Pattern Recognition (IAPR). For more details, see http://www.cs.umd.edu/~hjs

Virtualization and Databases: State of the Art and Research Challenges

Ashraf Aboulnaga, Cristiana Amza, Ken Salem
Please refer to individual short biographies for affiliations


The area of resource virtualization is currently receiving a lot of interest from academic and industrial researchers, since it can help in addressing many problems related to deploying, managing, and protecting software systems. Resource virtualization decouples the user's perception of hardware and software resources from the actual implementation of these resources. It adds a flexible and programmable layer of software between user applications and the resources that they use. This layer of software maps the virtual resources perceived by the applications to real physical resources. For example, storage virtualization makes it possible for organizations to store their data centrally on storage servers, while allowing applications to read and write this data as if it were on a local disk. The mapping from the virtual "disks" perceived by applications to the actual data on the storage server is managed by the storage virtualization layer.
The power of resource virtualization comes from the ability to manage the mapping from virtual resources to physical resources in the virtualization layer, and to change this mapping as needed. This is of interest to the database research community because resource virtualization, and the flexible mapping it introduces, can help solve many important problems in the area of database system manageability. At the same time, virtualizing database systems poses some unique research challenges.
The objective of this seminar is to provide an overview of resource virtualization, focusing on the potential it has for improving the deployment, management, and availability of database systems. We will present the unique challenges of virtualizing database systems and overview the ongoing research in related areas. We will also provide case studies of resource virtualization products and of how resource virtualization affects database systems. We will address the impact of resource virtualization on the interaction between database systems and other components of a typical software stack.
Short Bio for Ashraf Aboulnaga
Ashraf Aboulnaga is an Assistant Professor in the David R. Cheriton School of Computer Science at the University of Waterloo. His research interests are in the area of database management, with a current focus on self-managing database systems and wide area distributed data management. Ashraf obtained his PhD from the University of Wisconsin in 2002. Prior to joining the University of Waterloo, he was a Research Staff Member in the Data Management Department at the IBM Almaden Research Center in San Jose, California, from 2002 to 2004. Ashraf's ongoing research is investigating the use of resource virtualization to improve the deployment, manageability, and availability of database systems.
Short Bio for Cristiana Amza
Cristiana Amza is an assistant professor with the Department of Electrical and Computer Engineering at University of Toronto. Cristiana received her B.S. degree in Computer Engineering from Bucharest Polytechnic Institute in 1991, the M.S. and the Ph.D. degrees in Computer Science from Rice University in 1997 and 2003 respectively. Her research interests are in the design, implementation and evaluation of computer systems with an emphasis on autonomic computing, distributed systems and database systems. Her current work, as part of the Chameleon project at University of Toronto, focuses on infrastructure design for distributed systems that can automatically adapt to a changing environment and workload through self-managing, self-tuning and self-healing.
Short Bio for Ken Salem
Ken Salem is an associate professor in the David R. Cheriton School of Computer Science at the University of Waterloo. He received his B.Sc. in electrical engineering from Carnegie Mellon University, and his Ph.D. in computer science from Princeton University. Before coming to Waterloo, he spent several years as a member of the faculty at the University of Maryland, College Park, and he has spent sabbatical leaves at IBM's Almaden Research Center and at HP Laboratories in Palo Alto. His research interests span a variety of topics related to database management systems and storage systems.

Personalized Systems: Models and Methods from an IR and DB perspective

Yannis Ioannidis, Georgia Koutrika
Please refer to individual short biographies for affiliations


In today’s knowledge-driven society, information abundance and personal electronic device ubiquity have made it difficult for users to find the right information at the right time and at the right level of detail. To solve this problem, researchers have developed systems that adapt their behavior to the goals, tasks, interests, and other characteristics of their users. Based on models that capture important user characteristics, these personalized systems maintain their users’ profiles and take them into account to customize the content generated or its presentation to the different individuals.
This seminar will provide a comprehensive and cohesive overview of the state of the art and the key research results in the area of personalized systems emerging from two different research communities, Information Retrieval (IR) and Databases (DB). Its objective is to describe user models and personalization methods proposed by each community, addressing problems, solutions, and lessons learnt so far. In particular, the seminar will focus on the following issues: (a) Content Personalization Methods, including information filtering systems, collaborative filtering systems, recommender systems, continuous queries, personalized searches, and collaborative searches; (b) User Modeling, in particular, IR-based preference models and DB-based preference models; and (c) User Profiling Methods, including relevance feedback, machine learning, and web mining. The seminar will also discuss open issues and challenges in the area and will point to technology transfer opportunities between the IR and DB disciplines.
The target audience includes researchers and practitioners in database and web-based systems and applications.

Short Bio for Yannis Ioannidis
Yannis Ioannidis is a Professor at the Department of Informatics and Telecommunications of the University of Athens. Before that he was on the faculty of the Computer Sciences Department of the University of Wisconsin at Madison. He received his Diploma in Electrical Engineering from the National Technical University of Athens (1982), his M.Sc. in Applied Mathematics from Harvard University (1983), and his Ph.D. degree in Computer Science from the University of California at Berkeley (1986). His research interests include database and information systems, digital libraries, personalization, scientific systems and workflows, eHealth systems, and human-computer interaction. Yannis is an ACM Fellow (2004) and a recipient of the VLDB "10-Year Best Paper Award" (2003), the "Presidential Young Investigator Award" - PYI (1991), and of several awards for teaching excellence, including the nation-wide "Xanthopoulos-Pneumatikos Award for Excellence in Academic Teaching" in Greece (2006) and the "Chancellor's Award for Excellence in Teaching" at the University of Wisconsin (1996). He has also been a keynote or invited speaker in several conferences, including ICDE'07, and a PC (co-)chair of several conferences, including ICDE’09. Currently Yannis serves as the ACM Sigmod Vice-Chair. For more information, visit www.di.uoa.gr/~yannis.
Short Bio for Georgia Koutrika
Georgia Koutrika is currently a postdoctoral researcher at Infolab, Stanford University. She received her Ph.D. degree in 2005 from the Department of Informatics and Telecommunications of the University of Athens. She also holds a M.Sc. in Advanced Information Systems and a B.Sc. in Informatics from the same department. Her research interests include several aspects of information access in database systems, but also in information retrieval systems and digital libraries, such as personalization, keyword searching, user modeling and user profiling, and query processing and optimization. For more information, visit http://www.stanford.edu/~koutrika.

An Introduction to Knowledge Discovery Applications and Challenges in Life Sciences

Limsoon Wong
National University of Singapore

Disease profiles are changing in the world. E.g., cancer and heart disease are now the leading causes of death in many countries. Unfortunately these diseases often require extensive procedures and costly long-term therapies. To find more effectively combat these diseases, researchers have been applying powerful new high-throughput technologies for molecular analysis. Such technologies generate data at a tremendous rate. Coupled with advances in computing power, it is hoped that this flow of information should lead to improved understanding of biology and diseases, improved drug target, improved diagnostics, and improved treatment plan.
Yet with the deluge of information, it is difficult to make connections between different biomedical fields. Indeed, even though the raw sequence of the human genome has been assembled, it is but an equivalent of the table of contents for a book that has yet to be written. The majority of the genes have yet to be located, their primary structure have yet to be fully identified, their regulation and control have yet to be determined, their functions have yet to be fully elucidated, their product have yet to be described, and their linkage to diseases have yet to be understood. Similarly, many registries and databases capturing linkage and pathological data have proliferated in recent years. However, the correlation of this data with sequence and molecular biology data is typically unexplored.
Due to these trends and forces, bioinformatics technologies are now penetrating many aspects of research and development in biology and medicine. We focus on the role of knowledge discovery technologies in bioinformatics in this 3-hour seminar. We describe in detail a few example applications. We also suggest promising future directions in these applications. We plan to cover the following example applications: (1) diagnosis and understanding of diseases, (2) inference of protein functions, and (3) recognition of gene regulatory sites. At the end of this seminar, we hope the attendee would have gained an appreciation of these problems, their current state-of-art solutions, and future directions of their development.

Short Bio

Limsoon Wong is a Professor of Computing and Medicine at the National University of Singapore. He is currently working mostly on knowledge discovery technologies and is especially interested in their application to biomedicine. Prior to that, he has done significant research in database query language theory and finite model theory, as well as significant development work in broad-scale data integration systems. Limsoon has written about 100 research papers, a few of which are among the best cited of their respective fields. In recognition for his contributions to these fields, he has received several awards, the most recent being the 2003 FEER Asian Innovation Gold Award for his work on treatment optimization of childhood leukemias. He serves on the editorial boards of Journal of Bioinformatics and Computational Biology (ICP), Bioinformatics (OUP), and Drug Discovery Today (Elsevier). He is a scientific advisor to GeneticXchange (USA), Molecular Connections (India), and KOOPrime (Singapore).

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